Overview

Dataset statistics

Number of variables51
Number of observations28519
Missing cells50511
Missing cells (%)3.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.1 MiB
Average record size in memory408.0 B

Variable types

Numeric8
Categorical43

Alerts

all_symptoms has constant value "0"Constant
prefecture has a high cardinality: 74 distinct valuesHigh cardinality
city has a high cardinality: 773 distinct valuesHigh cardinality
town has a high cardinality: 2789 distinct valuesHigh cardinality
Unnamed: 0.1 is highly overall correlated with Unnamed: 0 and 1 other fieldsHigh correlation
Unnamed: 0 is highly overall correlated with Unnamed: 0.1 and 1 other fieldsHigh correlation
sick_day is highly overall correlated with con_steroid and 2 other fieldsHigh correlation
ivig_amount is highly overall correlated with con_steroid and 2 other fieldsHigh correlation
ivig_treatment_days is highly overall correlated with con_steroid and 3 other fieldsHigh correlation
no_days_passed_onset is highly overall correlated with sick_dayHigh correlation
flo is highly overall correlated with frlolHigh correlation
fclo is highly overall correlated with frclo and 1 other fieldsHigh correlation
frclo is highly overall correlated with fclo and 1 other fieldsHigh correlation
fclol is highly overall correlated with fclo and 1 other fieldsHigh correlation
frlol is highly overall correlated with floHigh correlation
lol is highly overall correlated with prefectureHigh correlation
prefecture is highly overall correlated with Unnamed: 0.1 and 2 other fieldsHigh correlation
father_history is highly overall correlated with parent_medical_history and 1 other fieldsHigh correlation
mother_hist is highly overall correlated with parent_medical_history and 1 other fieldsHigh correlation
parent_medical_history is highly overall correlated with father_history and 2 other fieldsHigh correlation
con_steroid is highly overall correlated with non_pulse and 4 other fieldsHigh correlation
non_pulse is highly overall correlated with prefecture and 5 other fieldsHigh correlation
immuno_agent is highly overall correlated with non_pulse and 3 other fieldsHigh correlation
caa_visit is highly overall correlated with non_pulse and 2 other fieldsHigh correlation
parents_kd is highly overall correlated with father_history and 2 other fieldsHigh correlation
additional_ig is highly overall correlated with immuno_agent and 2 other fieldsHigh correlation
IVIG_RES is highly overall correlated with con_steroid and 6 other fieldsHigh correlation
prefecture has 15156 (53.1%) missing valuesMissing
city has 16561 (58.1%) missing valuesMissing
town has 18794 (65.9%) missing valuesMissing
Unnamed: 0.1 is uniformly distributedUniform
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0.1 has unique valuesUnique
Unnamed: 0 has unique valuesUnique
month has 2367 (8.3%) zerosZeros
sick_day has 1312 (4.6%) zerosZeros
ivig_amount has 1317 (4.6%) zerosZeros
ivig_treatment_days has 1317 (4.6%) zerosZeros

Reproduction

Analysis started2022-12-14 08:08:47.900170
Analysis finished2022-12-14 08:09:49.838180
Duration1 minute and 1.94 second
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

Unnamed: 0.1
Real number (ℝ)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct28519
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14260
Minimum1
Maximum28519
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size222.9 KiB
2022-12-14T16:09:50.021497image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1426.9
Q17130.5
median14260
Q321389.5
95-th percentile27093.1
Maximum28519
Range28518
Interquartile range (IQR)14259

Descriptive statistics

Standard deviation8232.8705
Coefficient of variation (CV)0.57734015
Kurtosis-1.2
Mean14260
Median Absolute Deviation (MAD)7130
Skewness0
Sum4.0668094 × 108
Variance67780157
MonotonicityStrictly increasing
2022-12-14T16:09:50.278120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
19010 1
 
< 0.1%
19021 1
 
< 0.1%
19020 1
 
< 0.1%
19019 1
 
< 0.1%
19018 1
 
< 0.1%
19017 1
 
< 0.1%
19016 1
 
< 0.1%
19015 1
 
< 0.1%
19014 1
 
< 0.1%
Other values (28509) 28509
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
28519 1
< 0.1%
28518 1
< 0.1%
28517 1
< 0.1%
28516 1
< 0.1%
28515 1
< 0.1%
28514 1
< 0.1%
28513 1
< 0.1%
28512 1
< 0.1%
28511 1
< 0.1%
28510 1
< 0.1%

fr
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
0
28247 
1
 
272

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28247
99.0%
1 272
 
1.0%

Length

2022-12-14T16:09:50.523667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:09:50.761344image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28247
99.0%
1 272
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 28247
99.0%
1 272
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28247
99.0%
1 272
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28247
99.0%
1 272
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28247
99.0%
1 272
 
1.0%

fc
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
0
28412 
1
 
107

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28412
99.6%
1 107
 
0.4%

Length

2022-12-14T16:09:50.931727image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:09:51.139173image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28412
99.6%
1 107
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 28412
99.6%
1 107
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28412
99.6%
1 107
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28412
99.6%
1 107
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28412
99.6%
1 107
 
0.4%

flo
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
0
28440 
1
 
79

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28440
99.7%
1 79
 
0.3%

Length

2022-12-14T16:09:51.291214image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:09:51.463254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28440
99.7%
1 79
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 28440
99.7%
1 79
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28440
99.7%
1 79
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28440
99.7%
1 79
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28440
99.7%
1 79
 
0.3%

fl
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
0
28333 
1
 
186

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28333
99.3%
1 186
 
0.7%

Length

2022-12-14T16:09:51.628317image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:09:51.813173image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28333
99.3%
1 186
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 28333
99.3%
1 186
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28333
99.3%
1 186
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28333
99.3%
1 186
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28333
99.3%
1 186
 
0.7%

frc
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
0
27760 
1
 
759

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 27760
97.3%
1 759
 
2.7%

Length

2022-12-14T16:09:51.961232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:09:52.128392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 27760
97.3%
1 759
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 27760
97.3%
1 759
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 27760
97.3%
1 759
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 27760
97.3%
1 759
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 27760
97.3%
1 759
 
2.7%

frlo
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
0
28027 
1
 
492

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28027
98.3%
1 492
 
1.7%

Length

2022-12-14T16:09:52.271528image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:09:52.455442image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28027
98.3%
1 492
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 28027
98.3%
1 492
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28027
98.3%
1 492
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28027
98.3%
1 492
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28027
98.3%
1 492
 
1.7%

frl
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
0
28098 
1
 
421

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28098
98.5%
1 421
 
1.5%

Length

2022-12-14T16:09:52.618117image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:09:52.798342image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28098
98.5%
1 421
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 28098
98.5%
1 421
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28098
98.5%
1 421
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28098
98.5%
1 421
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28098
98.5%
1 421
 
1.5%

fclo
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
0
28021 
1
 
498

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28021
98.3%
1 498
 
1.7%

Length

2022-12-14T16:09:52.934199image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:09:53.100117image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28021
98.3%
1 498
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 28021
98.3%
1 498
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28021
98.3%
1 498
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28021
98.3%
1 498
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28021
98.3%
1 498
 
1.7%

fcl
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
0
28141 
1
 
378

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 28141
98.7%
1 378
 
1.3%

Length

2022-12-14T16:09:53.239319image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:09:53.402587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28141
98.7%
1 378
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 28141
98.7%
1 378
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28141
98.7%
1 378
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28141
98.7%
1 378
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28141
98.7%
1 378
 
1.3%

flol
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
0
28074 
1
 
445

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28074
98.4%
1 445
 
1.6%

Length

2022-12-14T16:09:53.548117image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:09:53.725516image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28074
98.4%
1 445
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 28074
98.4%
1 445
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28074
98.4%
1 445
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28074
98.4%
1 445
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28074
98.4%
1 445
 
1.6%

frclo
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
0
28021 
1
 
498

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28021
98.3%
1 498
 
1.7%

Length

2022-12-14T16:09:53.869043image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:09:54.031793image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28021
98.3%
1 498
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 28021
98.3%
1 498
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28021
98.3%
1 498
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28021
98.3%
1 498
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28021
98.3%
1 498
 
1.7%

fclol
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
0
28021 
1
 
498

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28021
98.3%
1 498
 
1.7%

Length

2022-12-14T16:09:54.169906image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:09:54.330956image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28021
98.3%
1 498
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 28021
98.3%
1 498
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28021
98.3%
1 498
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28021
98.3%
1 498
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28021
98.3%
1 498
 
1.7%

frlol
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
0
28440 
1
 
79

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28440
99.7%
1 79
 
0.3%

Length

2022-12-14T16:09:54.458104image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:09:54.626428image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28440
99.7%
1 79
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 28440
99.7%
1 79
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28440
99.7%
1 79
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28440
99.7%
1 79
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28440
99.7%
1 79
 
0.3%

frcl
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
0
28460 
1
 
59

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28460
99.8%
1 59
 
0.2%

Length

2022-12-14T16:09:54.768176image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:09:54.932386image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28460
99.8%
1 59
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 28460
99.8%
1 59
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28460
99.8%
1 59
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28460
99.8%
1 59
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28460
99.8%
1 59
 
0.2%

rc
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
0
28513 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28513
> 99.9%
1 6
 
< 0.1%

Length

2022-12-14T16:09:55.063172image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:09:55.232450image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28513
> 99.9%
1 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 28513
> 99.9%
1 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28513
> 99.9%
1 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28513
> 99.9%
1 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28513
> 99.9%
1 6
 
< 0.1%

rlo
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
0
28513 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28513
> 99.9%
1 6
 
< 0.1%

Length

2022-12-14T16:09:55.373210image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:09:55.568215image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28513
> 99.9%
1 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 28513
> 99.9%
1 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28513
> 99.9%
1 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28513
> 99.9%
1 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28513
> 99.9%
1 6
 
< 0.1%

rl
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
0
28517 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28517
> 99.9%
1 2
 
< 0.1%

Length

2022-12-14T16:09:55.710715image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:09:55.895265image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28517
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 28517
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28517
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28517
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28517
> 99.9%
1 2
 
< 0.1%

rclo
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
0
28501 
1
 
18

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28501
99.9%
1 18
 
0.1%

Length

2022-12-14T16:09:56.039352image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:09:56.206667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28501
99.9%
1 18
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 28501
99.9%
1 18
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28501
99.9%
1 18
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28501
99.9%
1 18
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28501
99.9%
1 18
 
0.1%

rcl
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
0
28516 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28516
> 99.9%
1 3
 
< 0.1%

Length

2022-12-14T16:09:56.348246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:09:56.540395image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28516
> 99.9%
1 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 28516
> 99.9%
1 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28516
> 99.9%
1 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28516
> 99.9%
1 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28516
> 99.9%
1 3
 
< 0.1%

rclol
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
0
28442 
1
 
77

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28442
99.7%
1 77
 
0.3%

Length

2022-12-14T16:09:56.689272image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:09:56.848569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28442
99.7%
1 77
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 28442
99.7%
1 77
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28442
99.7%
1 77
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28442
99.7%
1 77
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28442
99.7%
1 77
 
0.3%

clo
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
0
28518 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28518
> 99.9%
1 1
 
< 0.1%

Length

2022-12-14T16:09:56.988477image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:09:57.170796image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28518
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 28518
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28518
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28518
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28518
> 99.9%
1 1
 
< 0.1%

cl
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
0
28515 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28515
> 99.9%
1 4
 
< 0.1%

Length

2022-12-14T16:09:57.309542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:09:57.459569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28515
> 99.9%
1 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 28515
> 99.9%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28515
> 99.9%
1 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28515
> 99.9%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28515
> 99.9%
1 4
 
< 0.1%

clol
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
0
28508 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28508
> 99.9%
1 11
 
< 0.1%

Length

2022-12-14T16:09:57.600914image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:09:57.761513image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28508
> 99.9%
1 11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 28508
> 99.9%
1 11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28508
> 99.9%
1 11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28508
> 99.9%
1 11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28508
> 99.9%
1 11
 
< 0.1%

lol
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
0
28518 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28518
> 99.9%
1 1
 
< 0.1%

Length

2022-12-14T16:09:57.899941image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:09:58.071872image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28518
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 28518
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28518
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28518
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28518
> 99.9%
1 1
 
< 0.1%

all_symptoms
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
0
28519 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28519
100.0%

Length

2022-12-14T16:09:58.228322image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:09:58.403213image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28519
100.0%

Most occurring characters

ValueCountFrequency (%)
0 28519
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28519
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28519
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28519
100.0%

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct28519
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14260
Minimum1
Maximum28519
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size222.9 KiB
2022-12-14T16:09:58.570453image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1426.9
Q17130.5
median14260
Q321389.5
95-th percentile27093.1
Maximum28519
Range28518
Interquartile range (IQR)14259

Descriptive statistics

Standard deviation8232.8705
Coefficient of variation (CV)0.57734015
Kurtosis-1.2
Mean14260
Median Absolute Deviation (MAD)7130
Skewness0
Sum4.0668094 × 108
Variance67780157
MonotonicityStrictly increasing
2022-12-14T16:09:58.778360image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
19010 1
 
< 0.1%
19021 1
 
< 0.1%
19020 1
 
< 0.1%
19019 1
 
< 0.1%
19018 1
 
< 0.1%
19017 1
 
< 0.1%
19016 1
 
< 0.1%
19015 1
 
< 0.1%
19014 1
 
< 0.1%
Other values (28509) 28509
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
28519 1
< 0.1%
28518 1
< 0.1%
28517 1
< 0.1%
28516 1
< 0.1%
28515 1
< 0.1%
28514 1
< 0.1%
28513 1
< 0.1%
28512 1
< 0.1%
28511 1
< 0.1%
28510 1
< 0.1%

age
Real number (ℝ)

Distinct22
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3615484
Minimum2
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size222.9 KiB
2022-12-14T16:09:58.992020image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median5
Q36
95-th percentile10
Maximum25
Range23
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2200076
Coefficient of variation (CV)0.41406091
Kurtosis3.5813007
Mean5.3615484
Median Absolute Deviation (MAD)1
Skewness1.4408396
Sum152906
Variance4.9284338
MonotonicityNot monotonic
2022-12-14T16:09:59.170258image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
4 7047
24.7%
5 5540
19.4%
6 4154
14.6%
3 3993
14.0%
7 2826
9.9%
8 1654
 
5.8%
9 974
 
3.4%
2 903
 
3.2%
10 554
 
1.9%
11 336
 
1.2%
Other values (12) 538
 
1.9%
ValueCountFrequency (%)
2 903
 
3.2%
3 3993
14.0%
4 7047
24.7%
5 5540
19.4%
6 4154
14.6%
7 2826
9.9%
8 1654
 
5.8%
9 974
 
3.4%
10 554
 
1.9%
11 336
 
1.2%
ValueCountFrequency (%)
25 1
 
< 0.1%
23 1
 
< 0.1%
21 1
 
< 0.1%
20 1
 
< 0.1%
19 8
 
< 0.1%
18 10
 
< 0.1%
17 25
 
0.1%
16 32
0.1%
15 47
0.2%
14 72
0.3%

month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.457239
Minimum0
Maximum11
Zeros2367
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size222.9 KiB
2022-12-14T16:09:59.329367image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.4264619
Coefficient of variation (CV)0.62787462
Kurtosis-1.1890366
Mean5.457239
Median Absolute Deviation (MAD)3
Skewness0.026144412
Sum155635
Variance11.740641
MonotonicityNot monotonic
2022-12-14T16:09:59.846750image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
4 2557
9.0%
7 2483
8.7%
2 2453
8.6%
5 2420
8.5%
6 2394
8.4%
11 2391
8.4%
3 2388
8.4%
0 2367
8.3%
9 2338
8.2%
1 2293
8.0%
Other values (2) 4435
15.6%
ValueCountFrequency (%)
0 2367
8.3%
1 2293
8.0%
2 2453
8.6%
3 2388
8.4%
4 2557
9.0%
5 2420
8.5%
6 2394
8.4%
7 2483
8.7%
8 2247
7.9%
9 2338
8.2%
ValueCountFrequency (%)
11 2391
8.4%
10 2188
7.7%
9 2338
8.2%
8 2247
7.9%
7 2483
8.7%
6 2394
8.4%
5 2420
8.5%
4 2557
9.0%
3 2388
8.4%
2 2453
8.6%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
1
16235 
2
12284 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 16235
56.9%
2 12284
43.1%

Length

2022-12-14T16:10:00.050883image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:10:00.275196image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 16235
56.9%
2 12284
43.1%

Most occurring characters

ValueCountFrequency (%)
1 16235
56.9%
2 12284
43.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 16235
56.9%
2 12284
43.1%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 16235
56.9%
2 12284
43.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 16235
56.9%
2 12284
43.1%

prefecture
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct74
Distinct (%)0.6%
Missing15156
Missing (%)53.1%
Memory size222.9 KiB
東京都
1288 
東京
1034 
福岡
906 
愛知
 
790
神奈川
 
758
Other values (69)
8587 

Length

Max length6
Median length2
Mean length2.3872633
Min length2

Characters and Unicode

Total characters31901
Distinct characters88
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.1%

Sample

1st row北海道
2nd row北海道
3rd row北海道
4th row北海道
5th row北海道

Common Values

ValueCountFrequency (%)
東京都 1288
 
4.5%
東京 1034
 
3.6%
福岡 906
 
3.2%
愛知 790
 
2.8%
神奈川 758
 
2.7%
大阪 589
 
2.1%
北海道 514
 
1.8%
埼玉県 461
 
1.6%
静岡 456
 
1.6%
神奈川県 454
 
1.6%
Other values (64) 6113
21.4%
(Missing) 15156
53.1%

Length

2022-12-14T16:10:00.461787image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
東京都 1288
 
9.6%
東京 1035
 
7.7%
福岡 906
 
6.8%
愛知 790
 
5.9%
神奈川 758
 
5.7%
大阪 589
 
4.4%
北海道 514
 
3.8%
埼玉県 461
 
3.4%
静岡 456
 
3.4%
埼玉 454
 
3.4%
Other values (63) 6112
45.7%

Most occurring characters

ValueCountFrequency (%)
2592
 
8.1%
2323
 
7.3%
2017
 
6.3%
1677
 
5.3%
1557
 
4.9%
1415
 
4.4%
1319
 
4.1%
1212
 
3.8%
1062
 
3.3%
971
 
3.0%
Other values (78) 15756
49.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 31899
> 99.9%
Modifier Letter 1
 
< 0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2592
 
8.1%
2323
 
7.3%
2017
 
6.3%
1677
 
5.3%
1557
 
4.9%
1415
 
4.4%
1319
 
4.1%
1212
 
3.8%
1062
 
3.3%
971
 
3.0%
Other values (76) 15754
49.4%
Modifier Letter
ValueCountFrequency (%)
1
100.0%
Space Separator
ValueCountFrequency (%)
  1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 31887
> 99.9%
Katakana 12
 
< 0.1%
Common 2
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
2592
 
8.1%
2323
 
7.3%
2017
 
6.3%
1677
 
5.3%
1557
 
4.9%
1415
 
4.4%
1319
 
4.1%
1212
 
3.8%
1062
 
3.3%
971
 
3.0%
Other values (67) 15742
49.4%
Katakana
ValueCountFrequency (%)
2
16.7%
2
16.7%
2
16.7%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
Common
ValueCountFrequency (%)
1
50.0%
  1
50.0%

Most occurring blocks

ValueCountFrequency (%)
CJK 31883
99.9%
Katakana 13
 
< 0.1%
CJK Compat Ideographs 4
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

CJK
ValueCountFrequency (%)
2592
 
8.1%
2323
 
7.3%
2017
 
6.3%
1677
 
5.3%
1557
 
4.9%
1415
 
4.4%
1319
 
4.1%
1212
 
3.8%
1062
 
3.3%
971
 
3.0%
Other values (66) 15738
49.4%
CJK Compat Ideographs
ValueCountFrequency (%)
4
100.0%
Katakana
ValueCountFrequency (%)
2
15.4%
2
15.4%
2
15.4%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
None
ValueCountFrequency (%)
  1
100.0%

city
Categorical

HIGH CARDINALITY
MISSING

Distinct773
Distinct (%)6.5%
Missing16561
Missing (%)58.1%
Memory size222.9 KiB
横浜市
 
676
札幌市
 
297
さいたま市
 
289
広島市
 
278
福岡市
 
274
Other values (768)
10144 

Length

Max length7
Median length3
Mean length3.281987
Min length2

Characters and Unicode

Total characters39246
Distinct characters516
Distinct categories3 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique198 ?
Unique (%)1.7%

Sample

1st row札幌市
2nd row札幌市
3rd row札幌市
4th row札幌市
5th row札幌市

Common Values

ValueCountFrequency (%)
横浜市 676
 
2.4%
札幌市 297
 
1.0%
さいたま市 289
 
1.0%
広島市 278
 
1.0%
福岡市 274
 
1.0%
名古屋市 248
 
0.9%
浜松市 189
 
0.7%
京都市 181
 
0.6%
川崎市 172
 
0.6%
船橋市 163
 
0.6%
Other values (763) 9191
32.2%
(Missing) 16561
58.1%

Length

2022-12-14T16:10:00.711045image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
横浜市 676
 
5.7%
札幌市 297
 
2.5%
さいたま市 289
 
2.4%
広島市 278
 
2.3%
福岡市 274
 
2.3%
名古屋市 248
 
2.1%
浜松市 189
 
1.6%
京都市 181
 
1.5%
川崎市 172
 
1.4%
船橋市 163
 
1.4%
Other values (760) 9191
76.9%

Most occurring characters

ValueCountFrequency (%)
10793
27.5%
925
 
2.4%
725
 
1.8%
676
 
1.7%
654
 
1.7%
630
 
1.6%
564
 
1.4%
556
 
1.4%
552
 
1.4%
540
 
1.4%
Other values (506) 22631
57.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 39221
99.9%
Space Separator 16
 
< 0.1%
Modifier Letter 9
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10793
27.5%
925
 
2.4%
725
 
1.8%
676
 
1.7%
654
 
1.7%
630
 
1.6%
564
 
1.4%
556
 
1.4%
552
 
1.4%
540
 
1.4%
Other values (504) 22606
57.6%
Space Separator
ValueCountFrequency (%)
  16
100.0%
Modifier Letter
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 37168
94.7%
Hiragana 2022
 
5.2%
Katakana 40
 
0.1%
Common 16
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
10793
29.0%
925
 
2.5%
725
 
2.0%
676
 
1.8%
654
 
1.8%
630
 
1.7%
564
 
1.5%
556
 
1.5%
552
 
1.5%
540
 
1.5%
Other values (476) 20553
55.3%
Hiragana
ValueCountFrequency (%)
377
18.6%
311
15.4%
301
14.9%
289
14.3%
118
 
5.8%
100
 
4.9%
100
 
4.9%
97
 
4.8%
77
 
3.8%
55
 
2.7%
Other values (17) 197
9.7%
Katakana
ValueCountFrequency (%)
22
55.0%
18
45.0%
Common
ValueCountFrequency (%)
  16
100.0%

Most occurring blocks

ValueCountFrequency (%)
CJK 37159
94.7%
Hiragana 2022
 
5.2%
Katakana 40
 
0.1%
None 25
 
0.1%

Most frequent character per block

CJK
ValueCountFrequency (%)
10793
29.0%
925
 
2.5%
725
 
2.0%
676
 
1.8%
654
 
1.8%
630
 
1.7%
564
 
1.5%
556
 
1.5%
552
 
1.5%
540
 
1.5%
Other values (475) 20544
55.3%
Hiragana
ValueCountFrequency (%)
377
18.6%
311
15.4%
301
14.9%
289
14.3%
118
 
5.8%
100
 
4.9%
100
 
4.9%
97
 
4.8%
77
 
3.8%
55
 
2.7%
Other values (17) 197
9.7%
Katakana
ValueCountFrequency (%)
22
55.0%
18
45.0%
None
ValueCountFrequency (%)
  16
64.0%
9
36.0%

town
Categorical

HIGH CARDINALITY
MISSING

Distinct2789
Distinct (%)28.7%
Missing18794
Missing (%)65.9%
Memory size222.9 KiB
南区
 
287
中央区
 
248
西区
 
248
北区
 
242
東区
 
241
Other values (2784)
8459 

Length

Max length9
Median length3
Mean length2.9114653
Min length1

Characters and Unicode

Total characters28314
Distinct characters885
Distinct categories6 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1775 ?
Unique (%)18.3%

Sample

1st row豊平区
2nd row北区
3rd row東区
4th row東区
5th row中央区

Common Values

ValueCountFrequency (%)
南区 287
 
1.0%
中央区 248
 
0.9%
西区 248
 
0.9%
北区 242
 
0.8%
東区 241
 
0.8%
世田谷区 174
 
0.6%
練馬区 136
 
0.5%
大田区 114
 
0.4%
江東区 114
 
0.4%
青葉区 113
 
0.4%
Other values (2779) 7808
27.4%
(Missing) 18794
65.9%

Length

2022-12-14T16:10:00.946594image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
南区 287
 
3.0%
西区 248
 
2.6%
中央区 248
 
2.6%
北区 243
 
2.5%
東区 241
 
2.5%
世田谷区 175
 
1.8%
練馬区 136
 
1.4%
大田区 114
 
1.2%
江東区 114
 
1.2%
青葉区 113
 
1.2%
Other values (2777) 7806
80.3%

Most occurring characters

ValueCountFrequency (%)
5106
 
18.0%
2255
 
8.0%
708
 
2.5%
668
 
2.4%
651
 
2.3%
612
 
2.2%
575
 
2.0%
西 513
 
1.8%
503
 
1.8%
413
 
1.5%
Other values (875) 16310
57.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 28295
99.9%
Decimal Number 7
 
< 0.1%
Modifier Letter 6
 
< 0.1%
Other Punctuation 3
 
< 0.1%
Space Separator 2
 
< 0.1%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
5106
 
18.0%
2255
 
8.0%
708
 
2.5%
668
 
2.4%
651
 
2.3%
612
 
2.2%
575
 
2.0%
西 513
 
1.8%
503
 
1.8%
413
 
1.5%
Other values (866) 16291
57.6%
Decimal Number
ValueCountFrequency (%)
3 2
28.6%
2 2
28.6%
1
14.3%
1 1
14.3%
1
14.3%
Modifier Letter
ValueCountFrequency (%)
6
100.0%
Other Punctuation
ValueCountFrequency (%)
? 3
100.0%
Space Separator
ValueCountFrequency (%)
  2
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 27761
98.0%
Hiragana 348
 
1.2%
Katakana 192
 
0.7%
Common 13
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
5106
 
18.4%
2255
 
8.1%
708
 
2.6%
668
 
2.4%
651
 
2.3%
612
 
2.2%
575
 
2.1%
西 513
 
1.8%
503
 
1.8%
413
 
1.5%
Other values (807) 15757
56.8%
Hiragana
ValueCountFrequency (%)
91
26.1%
51
14.7%
35
 
10.1%
29
 
8.3%
16
 
4.6%
12
 
3.4%
10
 
2.9%
10
 
2.9%
8
 
2.3%
8
 
2.3%
Other values (31) 78
22.4%
Katakana
ValueCountFrequency (%)
106
55.2%
14
 
7.3%
12
 
6.2%
9
 
4.7%
9
 
4.7%
6
 
3.1%
6
 
3.1%
6
 
3.1%
6
 
3.1%
6
 
3.1%
Other values (9) 12
 
6.2%
Common
ValueCountFrequency (%)
? 3
23.1%
3 2
15.4%
2 2
15.4%
  2
15.4%
1
 
7.7%
1 1
 
7.7%
1
 
7.7%
1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
CJK 27755
98.0%
Hiragana 348
 
1.2%
Katakana 192
 
0.7%
None 10
 
< 0.1%
ASCII 8
 
< 0.1%
Geometric Shapes 1
 
< 0.1%

Most frequent character per block

CJK
ValueCountFrequency (%)
5106
 
18.4%
2255
 
8.1%
708
 
2.6%
668
 
2.4%
651
 
2.3%
612
 
2.2%
575
 
2.1%
西 513
 
1.8%
503
 
1.8%
413
 
1.5%
Other values (806) 15751
56.8%
Katakana
ValueCountFrequency (%)
106
55.2%
14
 
7.3%
12
 
6.2%
9
 
4.7%
9
 
4.7%
6
 
3.1%
6
 
3.1%
6
 
3.1%
6
 
3.1%
6
 
3.1%
Other values (9) 12
 
6.2%
Hiragana
ValueCountFrequency (%)
91
26.1%
51
14.7%
35
 
10.1%
29
 
8.3%
16
 
4.6%
12
 
3.4%
10
 
2.9%
10
 
2.9%
8
 
2.3%
8
 
2.3%
Other values (31) 78
22.4%
None
ValueCountFrequency (%)
6
60.0%
  2
 
20.0%
1
 
10.0%
1
 
10.0%
ASCII
ValueCountFrequency (%)
? 3
37.5%
3 2
25.0%
2 2
25.0%
1 1
 
12.5%
Geometric Shapes
ValueCountFrequency (%)
1
100.0%

father_history
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
1
28291 
2
 
228

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 28291
99.2%
2 228
 
0.8%

Length

2022-12-14T16:10:01.159664image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:10:01.373825image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 28291
99.2%
2 228
 
0.8%

Most occurring characters

ValueCountFrequency (%)
1 28291
99.2%
2 228
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 28291
99.2%
2 228
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 28291
99.2%
2 228
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 28291
99.2%
2 228
 
0.8%

mother_hist
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
1
28287 
2
 
232

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 28287
99.2%
2 232
 
0.8%

Length

2022-12-14T16:10:01.531142image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:10:01.738767image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 28287
99.2%
2 232
 
0.8%

Most occurring characters

ValueCountFrequency (%)
1 28287
99.2%
2 232
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 28287
99.2%
2 232
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 28287
99.2%
2 232
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 28287
99.2%
2 232
 
0.8%

sibling_hist
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
1
27850 
2
 
669

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 27850
97.7%
2 669
 
2.3%

Length

2022-12-14T16:10:01.911259image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:10:02.110100image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 27850
97.7%
2 669
 
2.3%

Most occurring characters

ValueCountFrequency (%)
1 27850
97.7%
2 669
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 27850
97.7%
2 669
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 27850
97.7%
2 669
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 27850
97.7%
2 669
 
2.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
1
28064 
2
 
455

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 28064
98.4%
2 455
 
1.6%

Length

2022-12-14T16:10:02.276814image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:10:02.478851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 28064
98.4%
2 455
 
1.6%

Most occurring characters

ValueCountFrequency (%)
1 28064
98.4%
2 455
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 28064
98.4%
2 455
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 28064
98.4%
2 455
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 28064
98.4%
2 455
 
1.6%

no_onset_occured
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
1
27228 
2
 
1288
0
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 27228
95.5%
2 1288
 
4.5%
0 3
 
< 0.1%

Length

2022-12-14T16:10:02.647096image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:10:02.858721image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 27228
95.5%
2 1288
 
4.5%
0 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 27228
95.5%
2 1288
 
4.5%
0 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 27228
95.5%
2 1288
 
4.5%
0 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 27228
95.5%
2 1288
 
4.5%
0 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 27228
95.5%
2 1288
 
4.5%
0 3
 
< 0.1%

con_steroid
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
1
23477 
2
3737 
0
 
1305

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23477
82.3%
2 3737
 
13.1%
0 1305
 
4.6%

Length

2022-12-14T16:10:03.018855image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:10:03.198770image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 23477
82.3%
2 3737
 
13.1%
0 1305
 
4.6%

Most occurring characters

ValueCountFrequency (%)
1 23477
82.3%
2 3737
 
13.1%
0 1305
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 23477
82.3%
2 3737
 
13.1%
0 1305
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 23477
82.3%
2 3737
 
13.1%
0 1305
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 23477
82.3%
2 3737
 
13.1%
0 1305
 
4.6%

non_pulse
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
1
19255 
0
6145 
2
3119 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 19255
67.5%
0 6145
 
21.5%
2 3119
 
10.9%

Length

2022-12-14T16:10:03.358466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:10:03.555668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 19255
67.5%
0 6145
 
21.5%
2 3119
 
10.9%

Most occurring characters

ValueCountFrequency (%)
1 19255
67.5%
0 6145
 
21.5%
2 3119
 
10.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 19255
67.5%
0 6145
 
21.5%
2 3119
 
10.9%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 19255
67.5%
0 6145
 
21.5%
2 3119
 
10.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 19255
67.5%
0 6145
 
21.5%
2 3119
 
10.9%

immuno_agent
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
0
15028 
1
12888 
2
 
603

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 15028
52.7%
1 12888
45.2%
2 603
 
2.1%

Length

2022-12-14T16:10:03.723014image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:10:03.901678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 15028
52.7%
1 12888
45.2%
2 603
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 15028
52.7%
1 12888
45.2%
2 603
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15028
52.7%
1 12888
45.2%
2 603
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15028
52.7%
1 12888
45.2%
2 603
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15028
52.7%
1 12888
45.2%
2 603
 
2.1%

sick_day
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7891581
Minimum0
Maximum38
Zeros1312
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size222.9 KiB
2022-12-14T16:10:04.047074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median5
Q36
95-th percentile8
Maximum38
Range38
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9078204
Coefficient of variation (CV)0.39836237
Kurtosis9.5568594
Mean4.7891581
Median Absolute Deviation (MAD)1
Skewness0.808327
Sum136582
Variance3.6397787
MonotonicityNot monotonic
2022-12-14T16:10:04.232507image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
5 8472
29.7%
4 7183
25.2%
6 4460
15.6%
3 2843
 
10.0%
7 2088
 
7.3%
0 1312
 
4.6%
8 835
 
2.9%
2 573
 
2.0%
9 359
 
1.3%
10 141
 
0.5%
Other values (17) 253
 
0.9%
ValueCountFrequency (%)
0 1312
 
4.6%
1 21
 
0.1%
2 573
 
2.0%
3 2843
 
10.0%
4 7183
25.2%
5 8472
29.7%
6 4460
15.6%
7 2088
 
7.3%
8 835
 
2.9%
9 359
 
1.3%
ValueCountFrequency (%)
38 1
 
< 0.1%
27 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 3
 
< 0.1%
21 3
 
< 0.1%
20 1
 
< 0.1%
19 5
< 0.1%
18 5
< 0.1%
17 9
< 0.1%

ivig_amount
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct295
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1889.3508
Minimum0
Maximum5000
Zeros1317
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size222.9 KiB
2022-12-14T16:10:04.458478image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1000
Q12000
median2000
Q32000
95-th percentile2000
Maximum5000
Range5000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation437.7784
Coefficient of variation (CV)0.23170837
Kurtosis13.454543
Mean1889.3508
Median Absolute Deviation (MAD)0
Skewness-3.8371098
Sum53882395
Variance191649.92
MonotonicityNot monotonic
2022-12-14T16:10:04.673632image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000 25713
90.2%
0 1317
 
4.6%
1000 444
 
1.6%
1900 64
 
0.2%
2100 56
 
0.2%
2083 32
 
0.1%
1800 30
 
0.1%
1923 27
 
0.1%
2200 22
 
0.1%
1940 20
 
0.1%
Other values (285) 794
 
2.8%
ValueCountFrequency (%)
0 1317
4.6%
80 1
 
< 0.1%
150 1
 
< 0.1%
400 3
 
< 0.1%
500 3
 
< 0.1%
900 2
 
< 0.1%
913 1
 
< 0.1%
960 1
 
< 0.1%
962 1
 
< 0.1%
964 1
 
< 0.1%
ValueCountFrequency (%)
5000 1
 
< 0.1%
3000 1
 
< 0.1%
2830 1
 
< 0.1%
2580 1
 
< 0.1%
2500 4
< 0.1%
2400 1
 
< 0.1%
2380 1
 
< 0.1%
2353 3
< 0.1%
2344 1
 
< 0.1%
2320 1
 
< 0.1%

ivig_treatment_days
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.96703952
Minimum0
Maximum5
Zeros1317
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size222.9 KiB
2022-12-14T16:10:04.858495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.24494448
Coefficient of variation (CV)0.25329314
Kurtosis21.227048
Mean0.96703952
Median Absolute Deviation (MAD)0
Skewness-1.3071436
Sum27579
Variance0.059997796
MonotonicityNot monotonic
2022-12-14T16:10:05.023194image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 26839
94.1%
0 1317
 
4.6%
2 356
 
1.2%
3 3
 
< 0.1%
5 3
 
< 0.1%
4 1
 
< 0.1%
ValueCountFrequency (%)
0 1317
 
4.6%
1 26839
94.1%
2 356
 
1.2%
3 3
 
< 0.1%
4 1
 
< 0.1%
5 3
 
< 0.1%
ValueCountFrequency (%)
5 3
 
< 0.1%
4 1
 
< 0.1%
3 3
 
< 0.1%
2 356
 
1.2%
1 26839
94.1%
0 1317
 
4.6%

caa_visit
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
0
14185 
1
13057 
2
 
1277

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
0 14185
49.7%
1 13057
45.8%
2 1277
 
4.5%

Length

2022-12-14T16:10:05.179240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:10:05.387535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 14185
49.7%
1 13057
45.8%
2 1277
 
4.5%

Most occurring characters

ValueCountFrequency (%)
0 14185
49.7%
1 13057
45.8%
2 1277
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14185
49.7%
1 13057
45.8%
2 1277
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14185
49.7%
1 13057
45.8%
2 1277
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14185
49.7%
1 13057
45.8%
2 1277
 
4.5%

redness_bcg
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
1
15235 
2
12140 
0
 
1144

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 15235
53.4%
2 12140
42.6%
0 1144
 
4.0%

Length

2022-12-14T16:10:05.549557image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:10:05.731424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 15235
53.4%
2 12140
42.6%
0 1144
 
4.0%

Most occurring characters

ValueCountFrequency (%)
1 15235
53.4%
2 12140
42.6%
0 1144
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 15235
53.4%
2 12140
42.6%
0 1144
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 15235
53.4%
2 12140
42.6%
0 1144
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 15235
53.4%
2 12140
42.6%
0 1144
 
4.0%

parents_kd
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
0
28064 
2
 
227
1
 
223
3
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28064
98.4%
2 227
 
0.8%
1 223
 
0.8%
3 5
 
< 0.1%

Length

2022-12-14T16:10:05.888777image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:10:06.058519image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28064
98.4%
2 227
 
0.8%
1 223
 
0.8%
3 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 28064
98.4%
2 227
 
0.8%
1 223
 
0.8%
3 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28064
98.4%
2 227
 
0.8%
1 223
 
0.8%
3 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28064
98.4%
2 227
 
0.8%
1 223
 
0.8%
3 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28064
98.4%
2 227
 
0.8%
1 223
 
0.8%
3 5
 
< 0.1%

no_days_passed_onset
Real number (ℝ)

Distinct35
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1391002
Minimum0
Maximum41
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size222.9 KiB
2022-12-14T16:10:06.238587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median4
Q35
95-th percentile7
Maximum41
Range41
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9908549
Coefficient of variation (CV)0.48098737
Kurtosis38.360422
Mean4.1391002
Median Absolute Deviation (MAD)1
Skewness3.4290042
Sum118043
Variance3.9635034
MonotonicityNot monotonic
2022-12-14T16:10:06.427760image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
4 7330
25.7%
3 6012
21.1%
5 5519
19.4%
2 3642
12.8%
6 2615
 
9.2%
7 1174
 
4.1%
1 1149
 
4.0%
8 494
 
1.7%
9 240
 
0.8%
10 100
 
0.4%
Other values (25) 244
 
0.9%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 1149
 
4.0%
2 3642
12.8%
3 6012
21.1%
4 7330
25.7%
5 5519
19.4%
6 2615
 
9.2%
7 1174
 
4.1%
8 494
 
1.7%
9 240
 
0.8%
ValueCountFrequency (%)
41 2
 
< 0.1%
40 2
 
< 0.1%
38 1
 
< 0.1%
35 1
 
< 0.1%
33 2
 
< 0.1%
31 1
 
< 0.1%
30 2
 
< 0.1%
29 2
 
< 0.1%
27 5
< 0.1%
25 1
 
< 0.1%

limb_changes
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
2
22885 
1
5634 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 22885
80.2%
1 5634
 
19.8%

Length

2022-12-14T16:10:06.618727image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:10:06.799078image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2 22885
80.2%
1 5634
 
19.8%

Most occurring characters

ValueCountFrequency (%)
2 22885
80.2%
1 5634
 
19.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 22885
80.2%
1 5634
 
19.8%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 22885
80.2%
1 5634
 
19.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 22885
80.2%
1 5634
 
19.8%

additional_ig
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
0
12426 
1
9942 
2
6151 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
0 12426
43.6%
1 9942
34.9%
2 6151
21.6%

Length

2022-12-14T16:10:06.936318image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:10:07.102903image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 12426
43.6%
1 9942
34.9%
2 6151
21.6%

Most occurring characters

ValueCountFrequency (%)
0 12426
43.6%
1 9942
34.9%
2 6151
21.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12426
43.6%
1 9942
34.9%
2 6151
21.6%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12426
43.6%
1 9942
34.9%
2 6151
21.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12426
43.6%
1 9942
34.9%
2 6151
21.6%

IVIG_RES
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
2
21669 
3
5545 
1
 
1305

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28519
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row3
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 21669
76.0%
3 5545
 
19.4%
1 1305
 
4.6%

Length

2022-12-14T16:10:07.258587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T16:10:07.423686image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2 21669
76.0%
3 5545
 
19.4%
1 1305
 
4.6%

Most occurring characters

ValueCountFrequency (%)
2 21669
76.0%
3 5545
 
19.4%
1 1305
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 21669
76.0%
3 5545
 
19.4%
1 1305
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
Common 28519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 21669
76.0%
3 5545
 
19.4%
1 1305
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 21669
76.0%
3 5545
 
19.4%
1 1305
 
4.6%

Interactions

2022-12-14T16:09:45.907905image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:35.050333image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:36.670777image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:38.412895image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:39.858017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:41.359154image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:42.870661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:44.427953image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:46.080848image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:35.294734image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:36.859094image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:38.591259image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:40.037755image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:41.541932image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:43.070716image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:44.638859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:46.243266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:35.487578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:37.037720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:38.778361image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:40.223906image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:41.754614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:43.272368image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:44.809829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:46.442115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:35.684257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:37.210069image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:38.948383image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:40.434616image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:41.920300image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:43.472673image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:44.979497image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:46.627918image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:35.887579image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:37.388812image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:39.104938image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:40.620940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:42.090847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:43.663142image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:45.147853image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:46.816774image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:36.091127image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:37.630733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:39.307860image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:40.819543image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:42.289212image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:43.867974image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:45.352351image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:47.021025image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:36.281705image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:38.031153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:39.493053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:41.002748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:42.487707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:44.064939image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:45.534580image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:47.193368image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:36.490162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:38.222188image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:39.694798image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:41.194739image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:42.677911image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:44.256308image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T16:09:45.720447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-12-14T16:10:07.674779image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-12-14T16:10:08.767246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-14T16:10:09.526386image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-14T16:10:10.280866image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-14T16:10:11.035667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-12-14T16:10:11.683902image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-14T16:09:47.680877image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-14T16:09:48.708263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-12-14T16:09:49.568156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0.1frfcfloflfrcfrlofrlfclofclflolfrclofclolfrlolfrclrcrlorlrclorclrclolcloclclollolall_symptomsUnnamed: 0agemonthgenderprefecturecitytownfather_historymother_histsibling_histparent_medical_historyno_onset_occuredcon_steroidnon_pulseimmuno_agentsick_dayivig_amountivig_treatment_dayscaa_visitredness_bcgparents_kdno_days_passed_onsetlimb_changesadditional_igIVIG_RES
0100000000000000000000000001512北海道札幌市豊平区111111113200011203112
1200000000000000000000000002572北海道札幌市北区111111114200011204212
23000000100000000000000000031322北海道札幌市東区111111115200012107123
3400000000000000000000000004551北海道札幌市東区111111114200011204112
4500000000100000000000000005701北海道札幌市中央区111111114200012102222
5600000000000000000000000006542北海道札幌市白石区111111116200011205212
6701000000000000000000000007672北海道札幌市東区111111115200011105123
78000100000000000000000000087111北海道札幌市中央区111111114200012102112
89000100000000000000000000091601北海道札幌市北区111121118100011108113
9100000000000000000000000000101451北海道札幌市中央区111111115200011105212
Unnamed: 0.1frfcfloflfrcfrlofrlfclofclflolfrclofclolfrlolfrclrcrlorlrclorclrclolcloclclollolall_symptomsUnnamed: 0agemonthgenderprefecturecitytownfather_historymother_histsibling_histparent_medical_historyno_onset_occuredcon_steroidnon_pulseimmuno_agentsick_dayivig_amountivig_treatment_dayscaa_visitredness_bcgparents_kdno_days_passed_onsetlimb_changesadditional_igIVIG_RES
2850928510000000000000000000000000028510382NaNNaNNaN111111105200010205202
2851028511000000000000000000000000028511371NaNNaNNaN111111104200010204202
2851128512000000000000000000000000028512302NaNNaNNaN111111107200010204123
2851228513000000010011000000000000028513781NaNNaNNaN111110000000105101
28513285140000000000000000000000000285143101NaNNaNNaN111111105200010205102
2851428515000000000000000000000000028515471NaNNaNNaN111111105200010103202
2851528516000000000100000000000000028516781NaNNaNNaN111111105200010103123
2851628517000000000000000000000000028517352NaNNaNNaN111111104200010203202
2851728518000000000000000000000000028518822NaNNaNNaN111111106220010104102
28518285190000000000000000000000000285192102NaNNaNNaN111111106190010201202